Overview

Dataset statistics

Number of variables21
Number of observations4119
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory675.9 KiB
Average record size in memory168.0 B

Variable types

NUM10
CAT10
BOOL1

Warnings

euribor3m is highly correlated with emp.var.rate and 1 other fieldsHigh correlation
emp.var.rate is highly correlated with euribor3mHigh correlation
nr.employed is highly correlated with euribor3mHigh correlation
previous has 3523 (85.5%) zeros Zeros

Reproduction

Analysis started2020-10-02 08:11:38.579598
Analysis finished2020-10-02 08:12:04.139149
Duration25.56 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

age
Real number (ℝ≥0)

Distinct67
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.11361981
Minimum18
Maximum88
Zeros0
Zeros (%)0.0%
Memory size32.2 KiB
2020-10-02T13:42:04.316068image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile26
Q132
median38
Q347
95-th percentile58
Maximum88
Range70
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.31336155
Coefficient of variation (CV)0.2571037367
Kurtosis0.4381297604
Mean40.11361981
Median Absolute Deviation (MAD)7
Skewness0.7156939791
Sum165228
Variance106.3654264
MonotocityNot monotonic
2020-10-02T13:42:04.541919image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
322165.2%
 
311914.6%
 
301774.3%
 
341744.2%
 
351724.2%
 
331704.1%
 
361684.1%
 
381503.6%
 
411473.6%
 
291393.4%
 
Other values (57)241558.6%
 
ValueCountFrequency (%) 
1830.1%
 
191< 0.1%
 
2040.1%
 
2170.2%
 
22100.2%
 
ValueCountFrequency (%) 
881< 0.1%
 
862< 0.1%
 
851< 0.1%
 
822< 0.1%
 
8130.1%
 

job
Categorical

Distinct12
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size32.2 KiB
admin.
1012 
blue-collar
884 
technician
691 
services
393 
management
324 
Other values (7)
815 
ValueCountFrequency (%) 
admin.101224.6%
 
blue-collar88421.5%
 
technician69116.8%
 
services3939.5%
 
management3247.9%
 
retired1664.0%
 
self-employed1593.9%
 
entrepreneur1483.6%
 
unemployed1112.7%
 
housemaid1102.7%
 
Other values (2)1212.9%
 
2020-10-02T13:42:04.742804image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-02T13:42:04.903714image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length10
Mean length8.992959456
Min length6

marital
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size32.2 KiB
married
2509 
single
1153 
divorced
446 
unknown
 
11
ValueCountFrequency (%) 
married250960.9%
 
single115328.0%
 
divorced44610.8%
 
unknown110.3%
 
2020-10-02T13:42:05.059643image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-02T13:42:05.162583image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:42:05.294508image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length7
Mean length6.828356397
Min length6

education
Categorical

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size32.2 KiB
university.degree
1264 
high.school
921 
basic.9y
574 
professional.course
535 
basic.4y
429 
Other values (3)
396 
ValueCountFrequency (%) 
university.degree126430.7%
 
high.school92122.4%
 
basic.9y57413.9%
 
professional.course53513.0%
 
basic.4y42910.4%
 
basic.6y2285.5%
 
unknown1674.1%
 
illiterate1< 0.1%
 
2020-10-02T13:42:05.463398image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-10-02T13:42:05.594318image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:42:05.767217image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length19
Median length11
Mean length12.82131585
Min length7

default
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size32.2 KiB
no
3315 
unknown
803 
yes
 
1
ValueCountFrequency (%) 
no331580.5%
 
unknown80319.5%
 
yes1< 0.1%
 
2020-10-02T13:42:05.926129image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-10-02T13:42:06.029088image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:42:06.143023image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length2
Mean length2.974993931
Min length2

housing
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size32.2 KiB
yes
2175 
no
1839 
unknown
 
105
ValueCountFrequency (%) 
yes217552.8%
 
no183944.6%
 
unknown1052.5%
 
2020-10-02T13:42:06.286922image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-02T13:42:06.378870image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:42:06.497803image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length3
Mean length2.655498908
Min length2

loan
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size32.2 KiB
no
3349 
yes
665 
unknown
 
105
ValueCountFrequency (%) 
no334981.3%
 
yes66516.1%
 
unknown1052.5%
 
2020-10-02T13:42:06.683713image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-02T13:42:06.789653image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:42:06.911583image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length2
Mean length2.288905074
Min length2

contact
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.2 KiB
cellular
2652 
telephone
1467 
ValueCountFrequency (%) 
cellular265264.4%
 
telephone146735.6%
 
2020-10-02T13:42:07.064478image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-02T13:42:07.176412image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:42:07.297343image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length8
Mean length8.356154406
Min length8

month
Categorical

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size32.2 KiB
may
1378 
jul
711 
aug
636 
jun
530 
nov
446 
Other values (5)
418 
ValueCountFrequency (%) 
may137833.5%
 
jul71117.3%
 
aug63615.4%
 
jun53012.9%
 
nov44610.8%
 
apr2155.2%
 
oct691.7%
 
sep641.6%
 
mar481.2%
 
dec220.5%
 
2020-10-02T13:42:07.532209image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-02T13:42:07.725102image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:42:07.945976image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

day_of_week
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size32.2 KiB
thu
860 
mon
855 
tue
841 
wed
795 
fri
768 
ValueCountFrequency (%) 
thu86020.9%
 
mon85520.8%
 
tue84120.4%
 
wed79519.3%
 
fri76818.6%
 
2020-10-02T13:42:08.117873image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-02T13:42:08.235806image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:42:08.365733image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

duration
Real number (ℝ≥0)

Distinct828
Distinct (%)20.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean256.7880554
Minimum0
Maximum3643
Zeros1
Zeros (%)< 0.1%
Memory size32.2 KiB
2020-10-02T13:42:08.527640image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile35
Q1103
median181
Q3317
95-th percentile740.2
Maximum3643
Range3643
Interquartile range (IQR)214

Descriptive statistics

Standard deviation254.7037361
Coefficient of variation (CV)0.9918831145
Kurtosis20.76192927
Mean256.7880554
Median Absolute Deviation (MAD)92
Skewness3.294781323
Sum1057710
Variance64873.99319
MonotocityNot monotonic
2020-10-02T13:42:08.741535image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
77240.6%
 
112230.6%
 
73220.5%
 
81210.5%
 
90200.5%
 
83200.5%
 
122200.5%
 
145200.5%
 
113200.5%
 
131190.5%
 
Other values (818)391094.9%
 
ValueCountFrequency (%) 
01< 0.1%
 
41< 0.1%
 
540.1%
 
650.1%
 
740.1%
 
ValueCountFrequency (%) 
36431< 0.1%
 
32531< 0.1%
 
26531< 0.1%
 
23011< 0.1%
 
19801< 0.1%
 

campaign
Real number (ℝ≥0)

Distinct25
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.537266327
Minimum1
Maximum35
Zeros0
Zeros (%)0.0%
Memory size32.2 KiB
2020-10-02T13:42:08.923414image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile7
Maximum35
Range34
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.568159238
Coefficient of variation (CV)1.012175667
Kurtosis25.28452046
Mean2.537266327
Median Absolute Deviation (MAD)1
Skewness4.003184952
Sum10451
Variance6.59544187
MonotocityNot monotonic
2020-10-02T13:42:09.073346image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%) 
1176442.8%
 
2103925.2%
 
354913.3%
 
42917.1%
 
51423.4%
 
6992.4%
 
7601.5%
 
8360.9%
 
9320.8%
 
10200.5%
 
Other values (15)872.1%
 
ValueCountFrequency (%) 
1176442.8%
 
2103925.2%
 
354913.3%
 
42917.1%
 
51423.4%
 
ValueCountFrequency (%) 
351< 0.1%
 
292< 0.1%
 
271< 0.1%
 
241< 0.1%
 
232< 0.1%
 

pdays
Real number (ℝ≥0)

Distinct21
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean960.4221899
Minimum0
Maximum999
Zeros2
Zeros (%)< 0.1%
Memory size32.2 KiB
2020-10-02T13:42:09.231256image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile999
Q1999
median999
Q3999
95-th percentile999
Maximum999
Range999
Interquartile range (IQR)0

Descriptive statistics

Standard deviation191.9227858
Coefficient of variation (CV)0.1998316863
Kurtosis20.81248388
Mean960.4221899
Median Absolute Deviation (MAD)0
Skewness-4.775139161
Sum3955979
Variance36834.35571
MonotocityNot monotonic
2020-10-02T13:42:09.387166image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%) 
999395996.1%
 
3521.3%
 
6421.0%
 
4140.3%
 
7100.2%
 
1080.2%
 
1250.1%
 
540.1%
 
240.1%
 
930.1%
 
Other values (11)180.4%
 
ValueCountFrequency (%) 
02< 0.1%
 
130.1%
 
240.1%
 
3521.3%
 
4140.3%
 
ValueCountFrequency (%) 
999395996.1%
 
211< 0.1%
 
191< 0.1%
 
182< 0.1%
 
171< 0.1%
 

previous
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1903374605
Minimum0
Maximum6
Zeros3523
Zeros (%)85.5%
Memory size32.2 KiB
2020-10-02T13:42:09.527086image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5417883234
Coefficient of variation (CV)2.846461868
Kurtosis22.12032347
Mean0.1903374605
Median Absolute Deviation (MAD)0
Skewness4.022978833
Sum784
Variance0.2935345874
MonotocityNot monotonic
2020-10-02T13:42:09.652999image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
0352385.5%
 
147511.5%
 
2781.9%
 
3250.6%
 
4140.3%
 
62< 0.1%
 
52< 0.1%
 
ValueCountFrequency (%) 
0352385.5%
 
147511.5%
 
2781.9%
 
3250.6%
 
4140.3%
 
ValueCountFrequency (%) 
62< 0.1%
 
52< 0.1%
 
4140.3%
 
3250.6%
 
2781.9%
 

poutcome
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size32.2 KiB
nonexistent
3523 
failure
454 
success
 
142
ValueCountFrequency (%) 
nonexistent352385.5%
 
failure45411.0%
 
success1423.4%
 
2020-10-02T13:42:09.832893image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-02T13:42:09.941831image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:42:10.291649image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length11
Median length11
Mean length10.42121874
Min length7

emp.var.rate
Real number (ℝ)

HIGH CORRELATION

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0849720806
Minimum-3.4
Maximum1.4
Zeros0
Zeros (%)0.0%
Memory size32.2 KiB
2020-10-02T13:42:10.428550image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-3.4
5-th percentile-2.9
Q1-1.8
median1.1
Q31.4
95-th percentile1.4
Maximum1.4
Range4.8
Interquartile range (IQR)3.2

Descriptive statistics

Standard deviation1.563114456
Coefficient of variation (CV)18.39562413
Kurtosis-1.041783886
Mean0.0849720806
Median Absolute Deviation (MAD)0.3
Skewness-0.7276878782
Sum350
Variance2.443326802
MonotocityNot monotonic
2020-10-02T13:42:10.575465image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.4162639.5%
 
-1.888321.4%
 
1.175818.4%
 
-0.13929.5%
 
-2.91644.0%
 
-3.41042.5%
 
-1.7872.1%
 
-1.1832.0%
 
-3210.5%
 
-0.21< 0.1%
 
ValueCountFrequency (%) 
-3.41042.5%
 
-3210.5%
 
-2.91644.0%
 
-1.888321.4%
 
-1.7872.1%
 
ValueCountFrequency (%) 
1.4162639.5%
 
1.175818.4%
 
-0.13929.5%
 
-0.21< 0.1%
 
-1.1832.0%
 

cons.price.idx
Real number (ℝ≥0)

Distinct26
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.5797043
Minimum92.201
Maximum94.767
Zeros0
Zeros (%)0.0%
Memory size32.2 KiB
2020-10-02T13:42:10.726380image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum92.201
5-th percentile92.713
Q193.075
median93.749
Q393.994
95-th percentile94.465
Maximum94.767
Range2.566
Interquartile range (IQR)0.919

Descriptive statistics

Standard deviation0.579348805
Coefficient of variation (CV)0.0061909664
Kurtosis-0.8233578937
Mean93.5797043
Median Absolute Deviation (MAD)0.38
Skewness-0.2166414217
Sum385454.802
Variance0.3356450378
MonotocityNot monotonic
2020-10-02T13:42:10.909274image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%) 
93.99475818.4%
 
93.91866716.2%
 
92.89359714.5%
 
93.44452812.8%
 
94.46543110.5%
 
93.23869.4%
 
93.0752014.9%
 
92.201751.8%
 
92.963751.8%
 
92.431431.0%
 
Other values (16)3588.7%
 
ValueCountFrequency (%) 
92.201751.8%
 
92.379250.6%
 
92.431431.0%
 
92.469140.3%
 
92.649360.9%
 
ValueCountFrequency (%) 
94.767240.6%
 
94.601200.5%
 
94.46543110.5%
 
94.215300.7%
 
94.199390.9%
 

cons.conf.idx
Real number (ℝ)

Distinct26
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-40.49910172
Minimum-50.8
Maximum-26.9
Zeros0
Zeros (%)0.0%
Memory size32.2 KiB
2020-10-02T13:42:11.078178image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-50.8
5-th percentile-47.1
Q1-42.7
median-41.8
Q3-36.4
95-th percentile-33.6
Maximum-26.9
Range23.9
Interquartile range (IQR)6.3

Descriptive statistics

Standard deviation4.594577507
Coefficient of variation (CV)-0.1134488745
Kurtosis-0.3143030044
Mean-40.49910172
Median Absolute Deviation (MAD)4.4
Skewness0.2873090796
Sum-166815.8
Variance21.11014247
MonotocityNot monotonic
2020-10-02T13:42:11.422983image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%) 
-36.475818.4%
 
-42.766716.2%
 
-46.259714.5%
 
-36.152812.8%
 
-41.843110.5%
 
-423869.4%
 
-47.12014.9%
 
-31.4751.8%
 
-40.8751.8%
 
-26.9431.0%
 
Other values (16)3588.7%
 
ValueCountFrequency (%) 
-50.8240.6%
 
-50250.6%
 
-49.5200.5%
 
-47.12014.9%
 
-46.259714.5%
 
ValueCountFrequency (%) 
-26.9431.0%
 
-29.8250.6%
 
-30.1360.9%
 
-31.4751.8%
 
-33210.5%
 

euribor3m
Real number (ℝ≥0)

HIGH CORRELATION

Distinct234
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.621355669
Minimum0.635
Maximum5.045
Zeros0
Zeros (%)0.0%
Memory size32.2 KiB
2020-10-02T13:42:11.759788image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.635
5-th percentile0.8084
Q11.334
median4.857
Q34.961
95-th percentile4.966
Maximum5.045
Range4.41
Interquartile range (IQR)3.627

Descriptive statistics

Standard deviation1.733591223
Coefficient of variation (CV)0.4787133276
Kurtosis-1.396366286
Mean3.621355669
Median Absolute Deviation (MAD)0.108
Skewness-0.7150798684
Sum14916.364
Variance3.005338527
MonotocityNot monotonic
2020-10-02T13:42:11.983663image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
4.8572746.7%
 
4.9632566.2%
 
4.9622375.8%
 
4.9612125.1%
 
4.8561383.4%
 
4.9651142.8%
 
4.9641102.7%
 
1.4051062.6%
 
4.961052.5%
 
4.9681012.5%
 
Other values (224)246659.9%
 
ValueCountFrequency (%) 
0.63530.1%
 
0.6361< 0.1%
 
0.6371< 0.1%
 
0.6392< 0.1%
 
0.641< 0.1%
 
ValueCountFrequency (%) 
5.0451< 0.1%
 
4.97210.5%
 
4.9681012.5%
 
4.967621.5%
 
4.966721.7%
 

nr.employed
Real number (ℝ≥0)

HIGH CORRELATION

Distinct11
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5166.481695
Minimum4963.6
Maximum5228.1
Zeros0
Zeros (%)0.0%
Memory size32.2 KiB
2020-10-02T13:42:12.184546image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum4963.6
5-th percentile5008.7
Q15099.1
median5191
Q35228.1
95-th percentile5228.1
Maximum5228.1
Range264.5
Interquartile range (IQR)129

Descriptive statistics

Standard deviation73.66790356
Coefficient of variation (CV)0.01425881439
Kurtosis0.0617241978
Mean5166.481695
Median Absolute Deviation (MAD)37.1
Skewness-1.075876888
Sum21280738.1
Variance5426.960015
MonotocityNot monotonic
2020-10-02T13:42:12.352448image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%) 
5228.1162639.5%
 
5099.182320.0%
 
519175818.4%
 
5195.83929.5%
 
5076.21644.0%
 
5017.51042.5%
 
4991.6872.1%
 
4963.6832.0%
 
5008.7601.5%
 
5023.5210.5%
 
ValueCountFrequency (%) 
4963.6832.0%
 
4991.6872.1%
 
5008.7601.5%
 
5017.51042.5%
 
5023.5210.5%
 
ValueCountFrequency (%) 
5228.1162639.5%
 
5195.83929.5%
 
519175818.4%
 
5176.31< 0.1%
 
5099.182320.0%
 

y
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.2 KiB
no
3668 
yes
451 
ValueCountFrequency (%) 
no366889.1%
 
yes45110.9%
 
2020-10-02T13:42:12.478376image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Interactions

2020-10-02T13:41:46.054278image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:46.246151image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:46.417053image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:46.589954image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:46.778845image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:46.946749image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:47.106655image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:47.267564image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:47.430491image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:47.590399image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:47.773295image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:47.950193image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:48.126074image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-10-02T13:41:48.732967image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:48.913863image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:49.077769image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:49.237678image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:49.396587image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:49.556495image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:49.728417image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:49.930301image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:50.103182image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:50.264090image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:50.427997image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:50.591905image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:50.791792image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:51.030651image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:51.190580image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:51.354467image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:51.550356image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:51.741246image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:51.988106image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:52.176997image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:52.334925image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:52.484840image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:52.637753image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:52.790665image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:52.959548image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:53.118476image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:53.283382image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:53.449267image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:53.610175image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:53.769084image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:53.935009image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:54.114888image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:54.271799image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:54.433706image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:54.585619image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:54.750525image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:54.908432image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:55.204266image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:55.358177image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:55.517087image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:55.668999image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:55.819932image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:55.968845image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:56.173715image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:56.348613image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:56.520514image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:56.684418image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:56.849321image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:57.008230image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:57.183132image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:57.340061image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:57.489975image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:57.643888image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:57.800777image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:57.948693image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:58.099608image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:58.279523image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:58.439431image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:58.606318image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:58.764238image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:58.921156image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:59.069052image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:59.226963image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:59.387889image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:59.535805image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:59.687717image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:41:59.850624image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:42:00.019508image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:42:00.177437image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:42:00.346341image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:42:00.503245image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:42:00.654164image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:42:00.808076image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:42:00.958990image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:42:01.107904image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:42:01.256819image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:42:01.438715image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:42:01.622591image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:42:01.843486image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:42:02.020361image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:42:02.172275image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:42:02.319190image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:42:02.486095image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:42:02.638009image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:42:02.932840image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-10-02T13:42:12.659274image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-10-02T13:42:13.168981image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-10-02T13:42:13.483821image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-10-02T13:42:13.848599image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-10-02T13:42:14.198392image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-10-02T13:42:03.305626image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-02T13:42:03.904284image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

agejobmaritaleducationdefaulthousingloancontactmonthday_of_weekdurationcampaignpdayspreviouspoutcomeemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedy
030blue-collarmarriedbasic.9ynoyesnocellularmayfri48729990nonexistent-1.892.893-46.21.3135099.1no
139servicessinglehigh.schoolnononotelephonemayfri34649990nonexistent1.193.994-36.44.8555191.0no
225servicesmarriedhigh.schoolnoyesnotelephonejunwed22719990nonexistent1.494.465-41.84.9625228.1no
338servicesmarriedbasic.9ynounknownunknowntelephonejunfri1739990nonexistent1.494.465-41.84.9595228.1no
447admin.marrieduniversity.degreenoyesnocellularnovmon5819990nonexistent-0.193.200-42.04.1915195.8no
532servicessingleuniversity.degreenononocellularsepthu12839992failure-1.194.199-37.50.8844963.6no
632admin.singleuniversity.degreenoyesnocellularsepmon29049990nonexistent-1.194.199-37.50.8794963.6no
741entrepreneurmarrieduniversity.degreeunknownyesnocellularnovmon4429990nonexistent-0.193.200-42.04.1915195.8no
831servicesdivorcedprofessional.coursenononocellularnovtue6819991failure-0.193.200-42.04.1535195.8no
935blue-collarmarriedbasic.9yunknownnonotelephonemaythu17019990nonexistent1.193.994-36.44.8555191.0no

Last rows

agejobmaritaleducationdefaulthousingloancontactmonthday_of_weekdurationcampaignpdayspreviouspoutcomeemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedy
410963retiredmarriedhigh.schoolnononocellularoctwed138619990nonexistent-3.492.431-26.90.7405017.5no
411053housemaiddivorcedbasic.6yunknownunknownunknowntelephonemayfri8529990nonexistent1.193.994-36.44.8555191.0no
411130technicianmarrieduniversity.degreenonoyescellularjunfri13119991failure-1.794.055-39.80.7484991.6no
411231techniciansingleprofessional.coursenoyesnocellularnovthu15519990nonexistent-0.193.200-42.04.0765195.8no
411331admin.singleuniversity.degreenoyesnocellularnovthu46319990nonexistent-0.193.200-42.04.0765195.8no
411430admin.marriedbasic.6ynoyesyescellularjulthu5319990nonexistent1.493.918-42.74.9585228.1no
411539admin.marriedhigh.schoolnoyesnotelephonejulfri21919990nonexistent1.493.918-42.74.9595228.1no
411627studentsinglehigh.schoolnononocellularmaymon6429991failure-1.892.893-46.21.3545099.1no
411758admin.marriedhigh.schoolnononocellularaugfri52819990nonexistent1.493.444-36.14.9665228.1no
411834managementsinglehigh.schoolnoyesnocellularnovwed17519990nonexistent-0.193.200-42.04.1205195.8no